Order Processing Based on Predicted Delay Time to Increase Likelihood of Order Fulfillment Within an Authorization Period

2. The apparatus of claim 1 wherein the one or more products comprises one or more computing devices.

3. The apparatus of claim 1 wherein the first machine learning model is further trained utilizing delayed order history information characterizing one or more of order type, shipment location, country of manufacturing factory, and delay codes.

4. The apparatus of claim 1 wherein determining whether the given order has at least the threshold likelihood of being delayed is based at least in part on determining delay attributed to issues relating to at least one of manufacturing order planning and manufacturing.

5. The apparatus of claim 1 wherein determining whether the given order has at least the threshold likelihood of being delayed is based at least in part on determining delay attributed to testing of the one or more products manufactured by the entity.

6. The apparatus of claim 5 wherein the delay attributed to the testing of the one or more products manufactured by the entity comprises delays associated with electro-mechanical repair of one or more issues identified during the testing of the one or more products manufactured by the entity.

7. The apparatus of claim 6 wherein the delays associated with the electro-mechanical repair of the one or more issues is based at least in part on at least one of electro-mechanical repair failure type, electro-mechanical repair resource availability, skill set of users performing the electro-mechanical repair, and a number of products in an electro-mechanical repair queue.

8. The apparatus of claim 1 wherein the second machine learning model utilizes (i) a support vector machine algorithm for classifying of the given order based at least in part on product, electro-mechanical repair task, one or more error codes, and time taken factors and (ii) conditional probability analysis for determining electro-mechanical repair time deviation based at least in part on a number of electro-mechanical repair tasks in an electro-mechanical repair queue.

9. The apparatus of claim 1 wherein the at least one action to reduce the predicted delay time comprises swapping one or more products of the given order with one or more similar products in one or more additional orders having associated authorization periods which end later than the authorization period of the given order.

10. The apparatus of claim 1 wherein the at least one action to reduce the predicted delay time comprises swapping, in an electro-mechanical repair queue, an ordering of one or more electro-mechanical repair tasks of the given order with one or more electro-mechanical repair tasks of one or more additional orders having associated authorization periods which end later than the authorization period of the given order.

11. The apparatus of claim 1 wherein the at least one action to extend the authorization period associated with the given order comprises requesting consent from the customer of the given order to extend the authorization period of the given order.

12. The apparatus of claim 1 wherein, responsive to failure of the one or more actions to at least one of reduce the predicted delay time and extend the authorization period associated with the given order, the at least one processing device is configured to utilize a third machine learning model for classifying a riskiness of the customer based at least in part on the customer's order history with the entity.

16. The computer program product of claim 14 wherein the program code when executed by the at least one processing device further causes the at least one processing device, responsive to failure of the one or more actions to at least one of reduce the predicted delay time and extend the authorization period associated with the given order, to utilize a third machine learning model for classifying a riskiness of the customer based at least in part on the customer's order history with the entity.

19. The method of claim 17 further comprising, responsive to failure of the one or more actions to at least one of reduce the predicted delay time and extend the authorization period associated with the given order, utilizing a third machine learning model for classifying a riskiness of the customer based at least in part on the customer's order history with the entity.